Nicholas H Chakiryan1, Ali Hajiran2, Youngchul Kim3, Ahmet M Aydin2, Logan Zemp2, Esther Katende2, Jonathan Nguyen4, Wenyi Fan5, Chia-Ho Cheng5, Neale Lopez-Blanco4, Jad Chahoud2, Philippe E Spiess2, Michelle Fournier2, Jasreman Dhillon4, Liang Wang6, Carlos Moran-Segura4, James Mulé7, Dongliang Du3, Sean J Yoder8, Anders Berglund3, Jamie K Teer3, Brandon J Manley9. 1. Department of Genitourinary Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Integrated Mathematical Oncology Department, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. Electronic address: Nicholas.chakiryan@moffitt.org. 2. Department of Genitourinary Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Integrated Mathematical Oncology Department, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 3. Department of Biostatistics and Bioinformatics, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Integrated Mathematical Oncology Department, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 4. Department of Pathology, H Lee Moffitt Cancer Center, Tampa, FL, USA; Integrated Mathematical Oncology Department, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 5. Biostatistics and Bioinformatics Shared Resource, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Integrated Mathematical Oncology Department, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 6. Department of Tumor Biology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Integrated Mathematical Oncology Department, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 7. Immunology Department, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Integrated Mathematical Oncology Department, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 8. Molecular Genomics Shared Resource, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Integrated Mathematical Oncology Department, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 9. Department of Genitourinary Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Department of Biostatistics and Bioinformatics, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Integrated Mathematical Oncology Department, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Abstract
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) tumors have low frequencies of genetic alterations compared with other malignancies, but very high levels of immune cell infiltration and favorable response rates to immunotherapy. Currently, the interplay between specific ccRCC somatic mutations and immune infiltration pattern is unclear. OBJECTIVE: To analyze the associations between common ccRCC somatic mutations and immune cell infiltration patterns within the tumor immune microenvironment (TIME). DESIGN, SETTING, AND PARTICIPANTS: The study included tumor samples (24 primary and 24 metastatic) from 48 patients with stage IV ccRCC. Targeted sequencing was performed for well-characterized recurrent somatic mutations in ccRCC, with the analysis focusing on the six most common ones: VHL, BAP1, PBRM1, SETD2, TP53, and KDM5C. For each sample, multiplex immunofluorescence (IF) was performed in lymphoid and myeloid panels, for seven regions of interest in three zones (tumor core, stroma, and tumor-stroma interface). IF-derived cellular densities were compared across patients, stratified by their somatic mutation status, using a linear mixed-model analysis. External validation was pursued using RNA-seq enrichment scoring from three large external data sources. RESULTS AND LIMITATIONS: Tumors with SETD2 mutations demonstrated significantly decreased levels of FOXP3+ T cells in the tumor core, stroma, and tumor-stroma interface. PBRM1 mutations were associated with decreased FOXP3+ T cells in the tumor core. Primary KDM5C mutations were associated with significantly increased CD206+ macrophage tumor infiltration in the tumor core. A computational method estimating immune cell types in the TIME using bulk RNA-seq data, xCell scoring, failed to validate associations from the IF analysis in large external data sets. A major limitation of the study is the relatively small patient population studied. CONCLUSIONS: This study provides evidence that common somatic mutations in ccRCC, such as SETD2, PBRM1, and KDM5C, are associated with distinct immune infiltration patterns within the TIME. PATIENT SUMMARY: In this study, we analyzed tumor samples from patients with metastatic kidney cancer to determine whether common genetic mutations that arise from the cancer cells are associated with the density of immune cells found within those tumors. We found several distinct immune cell patterns that were associated with specific genetic mutations. These findings provide insight into the interaction between cancer genetics and the immune system in kidney cancer.
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) tumors have low frequencies of genetic alterations compared with other malignancies, but very high levels of immune cell infiltration and favorable response rates to immunotherapy. Currently, the interplay between specific ccRCC somatic mutations and immune infiltration pattern is unclear. OBJECTIVE: To analyze the associations between common ccRCC somatic mutations and immune cell infiltration patterns within the tumor immune microenvironment (TIME). DESIGN, SETTING, AND PARTICIPANTS: The study included tumor samples (24 primary and 24 metastatic) from 48 patients with stage IV ccRCC. Targeted sequencing was performed for well-characterized recurrent somatic mutations in ccRCC, with the analysis focusing on the six most common ones: VHL, BAP1, PBRM1, SETD2, TP53, and KDM5C. For each sample, multiplex immunofluorescence (IF) was performed in lymphoid and myeloid panels, for seven regions of interest in three zones (tumor core, stroma, and tumor-stroma interface). IF-derived cellular densities were compared across patients, stratified by their somatic mutation status, using a linear mixed-model analysis. External validation was pursued using RNA-seq enrichment scoring from three large external data sources. RESULTS AND LIMITATIONS: Tumors with SETD2 mutations demonstrated significantly decreased levels of FOXP3+ T cells in the tumor core, stroma, and tumor-stroma interface. PBRM1 mutations were associated with decreased FOXP3+ T cells in the tumor core. Primary KDM5C mutations were associated with significantly increased CD206+ macrophage tumor infiltration in the tumor core. A computational method estimating immune cell types in the TIME using bulk RNA-seq data, xCell scoring, failed to validate associations from the IF analysis in large external data sets. A major limitation of the study is the relatively small patient population studied. CONCLUSIONS: This study provides evidence that common somatic mutations in ccRCC, such as SETD2, PBRM1, and KDM5C, are associated with distinct immune infiltration patterns within the TIME. PATIENT SUMMARY: In this study, we analyzed tumor samples from patients with metastatic kidney cancer to determine whether common genetic mutations that arise from the cancer cells are associated with the density of immune cells found within those tumors. We found several distinct immune cell patterns that were associated with specific genetic mutations. These findings provide insight into the interaction between cancer genetics and the immune system in kidney cancer.